(1) Field of the Invention
The present invention is directed to the detection and analysis of cracks, asymmetries, and imperfections within a material structure. In particular, the present invention is directed to a diagnostic tool that determines the linear and nonlinear characteristics of measured vibration signals in order to use the most appropriate signal processing technique for the identification of faults in structures and to determine quantitatively the predisposition of a structure to material failure.
(2) Description of the Prior Art
Dynamic loading, such as vibration, can be recorded by strain sensors. The strain sensor output signals in the form of time series data can be analyzed to determine the condition of a physical structure. Sensor signals from a “healthy” structure (i.e. a structure free of cracks, asymmetries and imperfections) are typically linear. The sensor signals from a structure with fatigue cracks and or other imperfections are typically nonlinear. The nonlinearity of time series data from sensors recording vibrations in structures with fatigue cracks can be classified as a discontinuity or as the simultaneous emission of several frequencies. With the advent of increased computational power in computers, there exists the computing capability to perform the calculations necessary to apply non-linear mathematical analysis techniques to time series data obtained from sensors, such as strain sensors.
There are several mathematical analysis techniques to determine the linear and/or nonlinear characteristics of strain sensor signals which represent the vibrations in a structure. One such technique is mutual information analysis. Mutual information analysis is a mathematical analysis technique derived from Information Theory. When dealing with random variables, a mutual information analysis will seek to determine the amount of information that one random variable contains about another random variable and vice versa. This sort of determination serves as a measure of dependence between the first and second random variables. Mutual information analysis can be used to test the dependencies between two sets of time series data, such as the data obtained from strain sensors.
Another mathematical analysis technique to determine the linear and nonlinear characteristics of strain sensor signals is wavelet analysis which is a linear mathematical analysis technique that can analyze discontinuities and edge effects. Wavelet analysis can define wavelets in either the real domain (referred to as real wavelets) or the complex domain (referred to as analytic wavelets). Real wavelets are suitable for identifying discontinuities and data compression. Analytic wavelets are suitable for capturing frequency content within a signal and therefore isolating simultaneous frequency emissions.
Other mathematical analysis techniques to determine the linear and nonlinear characteristics of strain sensor signals in the form of time series data are the surrogate data method, fast Fourier transforms and the phase space method. These methods are utilized (sometimes with other information or signal processing diagnostic techniques) to ascertain the linear or nonlinear characteristics of the measured data.
The above-described mathematical analysis techniques are capable of detecting the linear and/or nonlinear “behavior” of cracks, asymmetries, and imperfections within a material structure. In the past, methods for evaluating cracks and imperfections in material structures have relied primarily on linear mathematical analysis techniques as applied to time series data obtained from strain sensors (i.e. linear signal processing techniques), rather than non-linear mathematical analysis techniques (i.e., non-linear signal processing). Some linear signal processing techniques, however, fall short of identifying material failures or incongruities where such failures or incongruities are due to “nonlinearity” in the failure generating mechanism(s). In order to truly capture the range of potential material failures and discontinuities in a material structure, nonlinear signal processing techniques must also be considered to specifically determine any nonlinear behavior in the material structures. Determining the proper approach and combination of linear and nonlinear data signal processing techniques for evaluating structures in an efficient manner has become the challenge.